Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data

Abstract

Abstract. As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information. While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information. It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we breifly describe how Semplore is used for searching Wikipedia and an IBM customer's product information.

A Fully Distributed Scheme for Discovery of Semantic Relationships

“…Most of the current research on searching or querying Semantic Web uses an information retrieval (IR)-based search engine [10], [11], [14], [32]. The IR-based systems, such as Swoogle [10] and SWSE [14], index the Semantic Web by crawling and indexing the Semantic Web RDF documents that are found online and then offer a search interface over these documents.…”

Enhanced results for web search

“…Unlike web search engines, most of them focus on retrieving RDF resources directly instead of textual documents, while some engines perform hybrid retrieval on a collection of documents annotated with metadata, e.g. [34,14]. With the exception of [24], semantic search engines that perform their own crawling only retrieve data published directly as RDF, and do not extract RDFa or microformats.…”

SemSearchPro – Using semantics throughout the search process

“…In the physical implementation, there are different ways to manage and store graph-structured data like this. Databases (for managing graphs) such as RDF extensions to Oracle and IBM DB2, stores specifically built for RDF such as RDF-3X [28] and YARS [19], as well as IR-based technologies have been suggested [43]. For fast data access and for supporting search and ranking, specific indexes might be employed.…”

“…Indexes might be created to support different lookup patterns [19,42]. For entities representing documents (or any types of entities that are associated with text), an inverted index might be used for supporting keyword lookup and search [43].…”